B tech vi sem cse ml lecture 1 RTU Kota

09-05-2021 Mr. Himanshu Swarnkar, Department of CSE, GEC banswara

Students will try to learn: (Objective)
1. To introduce students to the basic concepts and techniques of Machine Learning.
2. To become familiar with Supervised, unsupervised, semi-supervised and reinforcement learning.
3. 3.To become familiar with Recommended system.
Students will be able to:(Outcome of the course)
1. Gain knowledge about basic concepts of Machine Learning
2. Identify machine learning techniques suitable for a given problem
3. Solve the problems using various machine learning techniques
4. Design application using machine learning techniques.
Scope of Machine Learning (ML):
Scope of ML is very vast in present and future.
Scope in various fields like medical, finance, social media, facial and voice recognition, online fraud detection, and
biometrics etc.
Cyber security is another area where we will see huge adoption of ML, which aids multi-layer protection.

Machine Learning
Unit-2
Lecture-1
Topic: Supervise Learning Algorithm

Definition of Machine Learning
Arthur Samuel (1959): Machine Learning is the field of study that gives the computer the ability to learn without
being explicitly programmed.
Tom Mitchell (1998): a computer program is said to learn from experience E with respect to some class of tasks T
and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Experience (data): games played by the program (with itself)
Performance measure: winning rate

What is Machine Learning?
Machine Learning is a subset of artificial intelligence which focuses mainly on machine learning from their
experience and making predictions based on its experience.
What does it do?
It enables the computers or the machines to make data-driven decisions rather than being explicitly programmed for
carrying out a certain task.
These programs or algorithms are designed in a way that they learn and improve over time when are exposed to new
data.
How does Machine Learning Work?
Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced
to the ML algorithm, it makes a prediction on the basis of the model.
The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning algorithm is
deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an
augmented training data set.

Types of Machine Learning
Machine learning is sub-categorized to three
types:
• 1. Supervised Learning - Train Me!
• 2. Unsupervised Learning – I am Self
sufficient in learning.
• 3. Semi Supervised Learning – Train
me Little Bit, Rest I train my self
• 4. Reinforcement learning – My life my
rules! (Hit & Trial)

1.Supervised Learning
Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which
acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a
prediction or decision when new data is given to it.

2 Unsupervised Learning
The model learns through observation and finds structures in the data. Once the model is given a dataset, it
automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to
the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.
Exl: Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based on some
patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the
model, it adds it to one of the created clusters

3 Semi-supervised Learning:
As the name suggests, its working lies between Supervised and Unsupervised techniques.
We use these techniques when we are dealing with a data which is a little bit labelled and rest large portion of it is
unlabeled.
We can use unsupervised technique to predict labels and then feed these labels to supervised techniques. This
technique is mostly applicable in case of image data-sets where usually all images are not labelled

4 Reinforcement Learning
It is the ability of an agent to interact with the environment and find out what is the best outcome.
It follows the concept of hit and trial method.
The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive
reward points gained the model trains itself.
And again once trained it gets ready to predict the new data presented to it.

1. Image Recognition:
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons,
places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend
tagging suggestion.
2. Speech Recognition
While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular
application of machine learning.
Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or
"Computer speech recognition." At present, machine learning algorithms are widely used by various applications of
speech recognition. Google assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow
the voice instructions.
3. Traffic prediction:
If we want to visit a new place, we take help of Google Maps, which shows us the correct path with the shortest route
and predicts the traffic conditions.
It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of
two ways:
Real Time location of the vehicle form Google Map app and sensors
Average time has taken on past days at the same time.

4. Product recommendations:
Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc.,
for product recommendation to the user. Whenever we search for some product on Amazon, then we started getting an
advertisement for the same product while internet surfing on the same browser and this is because of machine
learning.
5. Self-driving cars:
One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant
role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using
unsupervised learning method to train the car models to detect people and objects while driving.
6. Email Spam and Malware Filtering:
Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive an
important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind
this is Machine learning. Below are some spam filters used by Gmail:
• Content Filter
• Header filter
• General blacklists filter
• Rules-based filters
• Permission filters

7. Virtual Personal Assistant:
We have various virtual personal assistants such as Google assistant, Alexa, Cortana, Siri. As the name suggests,
they help us in finding the information using our voice instruction. These assistants can help us in various ways just
by our voice instructions such as Play music, call someone, Open an email, Scheduling an appointment, etc.
8. Online Fraud Detection:
Machine learning is making our online transaction safe and secure by detecting fraud transaction. Whenever we
perform some online transaction, there may be various ways that a fraudulent transaction can take place such as fake
accounts, fake ids, and steal money in the middle of a transaction. So to detect this, Feed Forward Neural
network helps us by checking whether it is a genuine transaction or a fraud transaction.
9. Stock Market trading:
Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs
in shares, so for this machine learning's long short term memory neural network is used for the prediction of stock
market trends.
10. Medical Diagnosis:
In medical science, machine learning is used for diseases diagnoses. With this, medical technology is growing very
fast and able to build 3D models that can predict the exact position of lesions in the brain.
It helps in finding brain tumors and other brain-related diseases easily.

11. Automatic Language Translation:
Nowadays, if we visit a new place and we are not aware of the language then it is not a problem at all, as for this also
machine learning helps us by converting the text into our known languages. Google's GNMT (Google Neural
Machine Translation) provide this feature, which is a Neural Machine Learning that translates the text into our
familiar language, and it called as automatic translation.

Thanks !

B tech vi sem cse ml lecture 1 RTU Kota

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